Clustering: Difference between revisions

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Some of the commonly used clustering algorithms are<ref name="pythonistaplanet">[https://pythonistaplanet.com/applications-of-unsupervised-learning/ Real World Applications of Unsupervised Learning]pythonistaplanet.com</ref>:
Some of the commonly used clustering algorithms are<ref name="pythonistaplanet">[https://pythonistaplanet.com/applications-of-unsupervised-learning/ Real World Applications of Unsupervised Learning]pythonistaplanet.com</ref>:


* [[K-means clustering|K-means]]:
* [[Expectation maximization]]:
* [[Hierarchical cluster analysis]] (HCA):
* [[Partitioned-baseed clustering]]<ref name="MLPython"/>:
* [[Partitioned-baseed clustering]]<ref name="MLPython"/>:
** [[K-means clustering|K-means]]:
** [[K-median]]:
** [[Fuzzy c-means]]:
* [[Hierarchical clustering]]<ref name="MLPython"/>:
* [[Hierarchical clustering]]<ref name="MLPython"/>:
** [[Agglomerative clustering]]:
** [[Divisive clustering]]:
* [[Density-based clustering]]<ref name="MLPython"/>:
* [[Density-based clustering]]<ref name="MLPython"/>:
** [[DBSCAN]]:
=== others ===
* [[Expectation maximization]]:
* [[Hierarchical cluster analysis]] (HCA):


== Applications ==
== Applications ==

Revision as of 22:05, 31 March 2020

Clustering is an unsupervised learning technique. It is used for grouping data points, or objects that are somehow similar. Clustering means finding clusters in a dataset, unsupervised.[1]

Types pof clustering

Some divide clustering into two subgroups[2]:

  • Hard clustering: Each data point either belongs to a cluster completely or not.
  • Soft clustering: A probability or likelihood is assigned for putting data points into separate clusters.

Clustering vs classification

Algorithms

Some of the commonly used clustering algorithms are[3]:


others

Applications

References

References